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Rexer Analytics’s Annual Data Miner Survey is the largest survey of data mining, data science, and analytics professionals in the industry. It consists of approximately 50 multiple choice and open-ended questions that cover seven general areas of data mining science and practice: (1) Field and goals, (2) Algorithms, (3) Models, (4) Tools (software packages used), (5) Technology, (6 ...
The ways in which data mining can be used can in some cases and contexts raise questions regarding privacy, legality, and ethics. [28] In particular, data mining government or commercial data sets for national security or law enforcement purposes, such as in the Total Information Awareness Program or in ADVISE, has raised privacy concerns. [29 ...
Challenges in Spatial mining: Geospatial data repositories tend to be very large. Moreover, existing GIS datasets are often splintered into feature and attribute components that are conventionally archived in hybrid data management systems.
While the analysis of educational data is not itself a new practice, recent advances in educational technology, including the increase in computing power and the ability to log fine-grained data about students' use of a computer-based learning environment, have led to an increased interest in developing techniques for analyzing the large amounts of data generated in educational settings.
Big data – Extremely large or complex datasets; Curse of dimensionality – Difficulties arising when analyzing data with many aspects ("dimensions") Combinatorial explosion – Rapid growth of the complexity of a problem due to its combinatorial properties; Data mining – Process of extracting and discovering patterns in large data sets
Data-driven pattern mining and knowledge discovery in databases [3] face such challenges that the discovered outputs are often not actionable. In the era of big data, how to effectively discover actionable insights from complex data and environment is critical. A significant paradigm shift is the evolution from data-driven pattern mining to ...
The proliferation of these more-efficient AI development techniques could ultimately pose greater security challenges than the concentrated development of chip-intensive systems by major state actors.
Social media mining faces grand challenges such as the big data paradox, obtaining sufficient samples, the noise removal fallacy, and evaluation dilemma. Social media mining represents the virtual world of social media in a computable way, measures it, and designs models that can help us understand its interactions.